InternAgent-1.5: A Unified Agentic Framework for Long-Horizon Autonomous Scientific Discovery
Shiyang Feng, Runmin Ma, Xiangchao Yan, Yue Fan, Yusong Hu, Songtao Huang, Shuaiyu Zhang, Zongsheng Cao, Tianshuo Peng, Jiakang Yuan, Zijie Guo, Zhijie Zhong, Shangheng Du, Weida Wang, Jinxin Shi, Yuhao Zhou, Xiaohan He, Zhiyin Yu, Fangchen Yu, Qihao Zheng, Jiamin Wu

TL;DR
InternAgent-1.5 is a comprehensive autonomous system that integrates generation, verification, and evolution to facilitate continuous, long-horizon scientific discovery across multiple domains, demonstrating strong performance on benchmarks and real-world tasks.
Contribution
It introduces a unified, scalable framework combining computational and empirical discovery methods with coordinated subsystems for end-to-end scientific research.
Findings
Achieves leading performance on scientific reasoning benchmarks.
Autonomously designs competitive machine learning algorithms.
Executes complete experiments producing novel scientific findings.
Abstract
We introduce InternAgent-1.5, a unified system designed for end-to-end scientific discovery across computational and empirical domains. The system is built on a structured architecture composed of three coordinated subsystems for generation, verification, and evolution. These subsystems are supported by foundational capabilities for deep research, solution optimization, and long horizon memory. The architecture allows InternAgent-1.5 to operate continuously across extended discovery cycles while maintaining coherent and improving behavior. It also enables the system to coordinate computational modeling and laboratory experimentation within a single unified system. We evaluate InternAgent-1.5 on scientific reasoning benchmarks such as GAIA, HLE, GPQA, and FrontierScience, and the system achieves leading performance that demonstrates strong foundational capabilities. Beyond these…
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Taxonomy
TopicsScientific Computing and Data Management · Machine Learning in Materials Science · Advanced Graph Neural Networks
